Skill UI

A comprehensive guide to positioning research contributions for top-tier financial microstructure journals, such as the Journal of Financial Markets (JFM). It teaches authors how to articulate their novel findings by anchoring them within established academic lineages (e.g., Glosten-Milgrom) and explicitly identifying the gap relative to 3-5 closest prior works. Focus must be placed on market mechanics and trading-process advances, rather than general financial outcomes.

A detailed guide for constructing a comprehensive robustness ledger in empirical financial research, particularly for microstructural data. It structures checks to address common academic threats, such as measurement bias, sample selection, microstructure noise (e.g., bid-ask bounce), and flawed inference, ensuring findings are methodologically sound and highly defensible in top-tier journals.

This guide provides rigorous best practices for authors presenting empirical evidence in microstructure finance papers. It ensures that tables and figures are self-contained, verifiable, and immediately convey the core mechanism and headline results to a skeptical academic referee, moving beyond simple descriptive statistics.

This router guides researchers through the entire lifecycle of a manuscript submitted to the Journal of Financial Markets (JFM). It provides a structured sequence of skills, from topic selection and literature positioning to empirical design, robustness checking, and rebuttal preparation. It ensures the focus remains strictly on market microstructure mechanisms, preventing common pitfalls like over-reliance on corporate finance models.

A specialized guide for authors submitting manuscripts to high-impact academic journals like JFQA. It provides a structured methodology for addressing detailed concerns from double-anonymous referees and the managing editor, covering advanced topics such as endogeneity, robust inference, economic magnitude, and ensuring full reproducibility for final acceptance.

A mandatory guide for authors submitting to the Journal of Financial and Quantitative Analysis (JFQA). This policy dictates the required archive structure, ensuring that all reported findings can be fully reproduced by depositing source code, raw or pseudo datasets, and a detailed execution roadmap in the JFQA Dataverse. It guarantees the highest standards of academic transparency and reproducibility in financial research.

This guide helps researchers structure the institutional background section of advanced finance manuscripts. It emphasizes moving beyond general descriptions to precisely articulate regulatory rules, market segmentation, and ownership structures. The goal is to transform descriptive text into clear evidence for identifying exogeneity and analyzing policy shocks in empirical studies.

This guide helps authors structure and strengthen their contribution claims for top-tier journals in economics and human resources. It emphasizes moving beyond simply stating new evidence by focusing on policy relevance, translating coefficients into natural human-capital units (like dollars or percentage points), and rigorously reconciling findings with prior literature to maximize impact.

A comprehensive guide for constructing and auditing empirical data pipelines, specifically tailored for high-impact journals. It covers advanced causal inference techniques (DID, RDD, IV, RCT), detailed sample management, robustness testing, and creating structured comparative estimates to withstand intense academic scrutiny.

This guide provides a comprehensive strategy for authors responding to a "Revise and Resubmit" (R&R) decision, particularly for high-stakes journals. It details the structure of the rebuttal letter, including handling conflicting referee comments, performing necessary reconciliation with prior literature, and integrating rigorous sensitivity tests (e.g., comparative estimation, pre-trend analysis). It focuses on strategic communication and method validation.

This comprehensive guide provides meticulous standards for presenting quantitative empirical results, particularly for academic journals like JHR. It covers the structure for main estimates, design diagnostics (pre-trends, balance), reconciliation tables, and organizing the online appendix. Learn how to maximize clarity and impact while adhering to strict page limits.

This guide provides a comprehensive framework for conducting rigorous cross-national data analysis, essential for academic research in international business. It covers critical advanced statistical methods like Measurement Invariance Testing (MGCFA), Multilevel Modeling (HLM), and Multi-Group SEM. It instructs users on handling cross-level moderation, interpreting country-level effects, and ensuring results are statistically auditable across complex data structures (e.g., panels, dyads).